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دانلود کتاب MATLAB Deep Learning Toolbox™ User's Guide

دانلود کتاب راهنمای کاربر MATLAB Deep Learning Toolbox™

MATLAB Deep Learning Toolbox™ User's Guide

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MATLAB Deep Learning Toolbox™ User's Guide

ویرایش: R2020a 
نویسندگان:   
سری:  
 
ناشر: The MathWorks, Inc. 
سال نشر: 2020 
تعداد صفحات: 2192 
زبان: English 
فرمت فایل : PDF (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود) 
حجم فایل: 60 مگابایت 

قیمت کتاب (تومان) : 55,000



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فهرست مطالب

Deep Networks
	Deep Learning in MATLAB
		What Is Deep Learning?
		Try Deep Learning in 10 Lines of MATLAB Code
		Start Deep Learning Faster Using Transfer Learning
		Train Classifiers Using Features Extracted from Pretrained Networks
		Deep Learning with Big Data on CPUs, GPUs, in Parallel, and on the Cloud
	Deep Learning with Big Data on GPUs and in Parallel
		Training with Multiple GPUs
		Deep Learning in the Cloud
		Fetch and Preprocess Data in Background
	Pretrained Deep Neural Networks
		Compare Pretrained Networks
		Load Pretrained Networks
		Feature Extraction
		Transfer Learning
		Import and Export Networks
	Learn About Convolutional Neural Networks
	Multiple-Input and Multiple-Output Networks
		Multiple-Input Networks
		Multiple-Output Networks
	List of Deep Learning Layers
		Deep Learning Layers
	Specify Layers of Convolutional Neural Network
		Image Input Layer
		Convolutional Layer
		Batch Normalization Layer
		ReLU Layer
		Cross Channel Normalization (Local Response Normalization) Layer
		Max and Average Pooling Layers
		Dropout Layer
		Fully Connected Layer
		Output Layers
	Set Up Parameters and Train Convolutional Neural Network
		Specify Solver and Maximum Number of Epochs
		Specify and Modify Learning Rate
		Specify Validation Data
		Select Hardware Resource
		Save Checkpoint Networks and Resume Training
		Set Up Parameters in Convolutional and Fully Connected Layers
		Train Your Network
	Deep Learning Tips and Tricks
		Choose Network Architecture
		Choose Training Options
		Improve Training Accuracy
		Fix Errors in Training
		Prepare and Preprocess Data
		Use Available Hardware
		Fix Errors With Loading from MAT-Files
	Long Short-Term Memory Networks
		LSTM Network Architecture
		Layers
		Classification, Prediction, and Forecasting
		Sequence Padding, Truncation, and Splitting
		Normalize Sequence Data
		Out-of-Memory Data
		Visualization
		LSTM Layer Architecture
Deep Network Designer
	Transfer Learning with Deep Network Designer
	Build Networks with Deep Network Designer
		Open App and Import Networks
		Create and Edit a Network
		Check Network
		Train Network Using Deep Network Designer
		Export Network
	Create Simple Sequence Classification Network Using Deep Network Designer
	Generate MATLAB Code from Deep Network Designer
		Generate MATLAB Code to Recreate Network Layers
		Generate MATLAB Code to Train Network
Deep Learning with Images
	Classify Webcam Images Using Deep Learning
	Train Deep Learning Network to Classify New Images
	Train Residual Network for Image Classification
	Classify Image Using GoogLeNet
	Extract Image Features Using Pretrained Network
	Transfer Learning Using AlexNet
	Create Simple Deep Learning Network for Classification
	Train Convolutional Neural Network for Regression
	Train Network with Multiple Outputs
	Convert Classification Network into Regression Network
	Train Generative Adversarial Network (GAN)
	Train Conditional Generative Adversarial Network (CGAN)
	Train a Siamese Network to Compare Images
	Train a Siamese Network for Dimensionality Reduction
	Train Variational Autoencoder (VAE) to Generate Images
Deep Learning with Time Series, Sequences, and Text
	Sequence Classification Using Deep Learning
	Time Series Forecasting Using Deep Learning
	Speech Command Recognition Using Deep Learning
	Sequence-to-Sequence Classification Using Deep Learning
	Sequence-to-Sequence Regression Using Deep Learning
	Classify Videos Using Deep Learning
	Sequence-to-Sequence Classification Using 1-D Convolutions
	Classify Text Data Using Deep Learning
	Classify Text Data Using Convolutional Neural Network
	Multilabel Text Classification Using Deep Learning
	Sequence-to-Sequence Translation Using Attention
	Generate Text Using Deep Learning
	Pride and Prejudice and MATLAB
	Word-By-Word Text Generation Using Deep Learning
	Image Captioning Using Attention
Deep Learning Tuning and Visualization
	Deep Dream Images Using GoogLeNet
	Grad-CAM Reveals the Why Behind Deep Learning Decisions
	Understand Network Predictions Using Occlusion
	Investigate Classification Decisions Using Gradient Attribution Techniques
	Resume Training from Checkpoint Network
	Deep Learning Using Bayesian Optimization
	Run Multiple Deep Learning Experiments in Parallel
	Monitor Deep Learning Training Progress
	Customize Output During Deep Learning Network Training
	Investigate Network Predictions Using Class Activation Mapping
	View Network Behavior Using tsne
	Visualize Activations of a Convolutional Neural Network
	Visualize Activations of LSTM Network
	Visualize Features of a Convolutional Neural Network
	Visualize Image Classifications Using Maximal and Minimal Activating Images
	Monitor GAN Training Progress and Identify Common Failure Modes
		Convergence Failure
		Mode Collapse
Manage Deep Learning Experiments
	Create a Deep Learning Experiment for Classification
	Create a Deep Learning Experiment for Regression
	Evaluate Deep Learning Experiments by Using Metric Functions
	Try Multiple Pretrained Networks for Transfer Learning
	Experiment with Weight Initializers for Transfer Learning
Deep Learning in Parallel and the Cloud
	Scale Up Deep Learning in Parallel and in the Cloud
		Deep Learning on Multiple GPUs
		Deep Learning in the Cloud
		Advanced Support for Fast Multi-Node GPU Communication
	Deep Learning with MATLAB on Multiple GPUs
		Select Particular GPUs to Use for Training
		Train Network in the Cloud Using Automatic Parallel Support
	Train Network in the Cloud Using Automatic Parallel Support
	Use parfeval to Train Multiple Deep Learning Networks
	Send Deep Learning Batch Job to Cluster
	Train Network Using Automatic Multi-GPU Support
	Use parfor to Train Multiple Deep Learning Networks
	Upload Deep Learning Data to the Cloud
	Train Network in Parallel with Custom Training Loop
Computer Vision Examples
	Point Cloud Classification Using PointNet Deep Learning
	Import Pretrained ONNX YOLO v2 Object Detector
	Export YOLO v2 Object Detector to ONNX
	Object Detection Using SSD Deep Learning
	Object Detection Using YOLO v3 Deep Learning
	Object Detection Using YOLO v2 Deep Learning
	Semantic Segmentation Using Deep Learning
	Semantic Segmentation Using Dilated Convolutions
	Semantic Segmentation of Multispectral Images Using Deep Learning
	3-D Brain Tumor Segmentation Using Deep Learning
	Define Custom Pixel Classification Layer with Tversky Loss
	Train Object Detector Using R-CNN Deep Learning
	Object Detection Using Faster R-CNN Deep Learning
Image Processing Examples
	Remove Noise from Color Image Using Pretrained Neural Network
	Single Image Super-Resolution Using Deep Learning
	JPEG Image Deblocking Using Deep Learning
	Image Processing Operator Approximation Using Deep Learning
	Deep Learning Classification of Large Multiresolution Images
	Generate Image from Segmentation Map Using Deep Learning
	Neural Style Transfer Using Deep Learning
Automated Driving Examples
	Train a Deep Learning Vehicle Detector
	Create Occupancy Grid Using Monocular Camera and Semantic Segmentation
Signal Processing Examples
	Radar Waveform Classification Using Deep Learning
	Pedestrian and Bicyclist Classification Using Deep Learning
	Label QRS Complexes and R Peaks of ECG Signals Using Deep Network
	Waveform Segmentation Using Deep Learning
	Modulation Classification with Deep Learning
	Classify ECG Signals Using Long Short-Term Memory Networks
	Classify Time Series Using Wavelet Analysis and Deep Learning
Audio Examples
	Train Generative Adversarial Network (GAN) for Sound Synthesis
	Sequential Feature Selection for Audio Features
	Acoustic Scene Recognition Using Late Fusion
	Keyword Spotting in Noise Using MFCC and LSTM Networks
	Speech Emotion Recognition
	Spoken Digit Recognition with Wavelet Scattering and Deep Learning
	Cocktail Party Source Separation Using Deep Learning Networks
	Voice Activity Detection in Noise Using Deep Learning
	Denoise Speech Using Deep Learning Networks
	Classify Gender Using LSTM Networks
Reinforcement Learning Examples
	Create Simulink Environment and Train Agent
	Train DDPG Agent to Swing Up and Balance Pendulum with Image Observation
	Create Agent Using Deep Network Designer and Train Using Image Observations
	Train DDPG Agent to Control Flying Robot
	Train Biped Robot to Walk Using Reinforcement Learning Agents
	Train DDPG Agent for Adaptive Cruise Control
	Train DQN Agent for Lane Keeping Assist Using Parallel Computing
	Train DDPG Agent for Path Following Control
Predictive Maintenance Examples
	Chemical Process Fault Detection Using Deep Learning
Automatic Differentiation
	Define Custom Deep Learning Layers
		Layer Templates
		Intermediate Layer Architecture
		Check Validity of Layer
		Include Layer in Network
		Output Layer Architecture
	Define Custom Deep Learning Layer with Learnable Parameters
		Layer with Learnable Parameters Template
		Name the Layer
		Declare Properties and Learnable Parameters
		Create Constructor Function
		Create Forward Functions
		Completed Layer
		GPU Compatibility
		Check Validity of Layer Using checkLayer
		Include Custom Layer in Network
	Define Custom Deep Learning Layer with Multiple Inputs
		Layer with Learnable Parameters Template
		Name the Layer
		Declare Properties and Learnable Parameters
		Create Constructor Function
		Create Forward Functions
		Completed Layer
		GPU Compatibility
		Check Validity of Layer with Multiple Inputs
		Use Custom Weighted Addition Layer in Network
	Define Custom Classification Output Layer
		Classification Output Layer Template
		Name the Layer
		Declare Layer Properties
		Create Constructor Function
		Create Forward Loss Function
		Completed Layer
		GPU Compatibility
		Check Output Layer Validity
		Include Custom Classification Output Layer in Network
	Define Custom Weighted Classification Layer
		Classification Output Layer Template
		Name the Layer
		Declare Layer Properties
		Create Constructor Function
		Create Forward Loss Function
		Completed Layer
		GPU Compatibility
		Check Output Layer Validity
	Define Custom Regression Output Layer
		Regression Output Layer Template
		Name the Layer
		Declare Layer Properties
		Create Constructor Function
		Create Forward Loss Function
		Completed Layer
		GPU Compatibility
		Check Output Layer Validity
		Include Custom Regression Output Layer in Network
	Specify Custom Layer Backward Function
		Create Custom Layer
		Create Backward Function
		Complete Layer
		GPU Compatibility
	Specify Custom Output Layer Backward Loss Function
		Create Custom Layer
		Create Backward Loss Function
		Complete Layer
		GPU Compatibility
	Check Custom Layer Validity
		Check Layer Validity
		List of Tests
		Generated Data
		Diagnostics
	Specify Custom Weight Initialization Function
	Compare Layer Weight Initializers
	Assemble Network from Pretrained Keras Layers
	Assemble Multiple-Output Network for Prediction
	Automatic Differentiation Background
		What Is Automatic Differentiation?
		Forward Mode
		Reverse Mode
	Use Automatic Differentiation In Deep Learning Toolbox
		Custom Training and Calculations Using Automatic Differentiation
		Use dlgradient and dlfeval Together for Automatic Differentiation
		Derivative Trace
		Characteristics of Automatic Derivatives
	Define Custom Training Loops, Loss Functions, and Networks
		Define Custom Training Loops
		Define Custom Networks
	Specify Training Options in Custom Training Loop
		Solver Options
		Learn Rate
		Plots
		Verbose Output
		Mini-Batch Size
		Number of Epochs
		Validation
		L2 Regularization
		Gradient Clipping
		Single CPU or GPU Training
		Checkpoints
	Train Network Using Custom Training Loop
	Update Batch Normalization Statistics in Custom Training Loop
	Make Predictions Using dlnetwork Object
	Train Network Using Model Function
	Update Batch Normalization Statistics Using Model Function
	Make Predictions Using Model Function
	Train Network Using Cyclical Learn Rate for Snapshot Ensembling
	List of Functions with dlarray Support
		Deep Learning Toolbox Functions with dlarray Support
		MATLAB Functions with dlarray Support
		Notable dlarray Behaviors
Deep Learning Data Preprocessing
	Datastores for Deep Learning
		Select Datastore
		Input Datastore for Training, Validation, and Inference
		Specify Read Size and Mini-Batch Size
		Transform and Combine Datastores
		Use Datastore for Parallel Training and Background Dispatching
	Preprocess Images for Deep Learning
		Resize Images Using Rescaling and Cropping
		Augment Images for Training with Random Geometric Transformations
		Perform Additional Image Processing Operations Using Built-In Datastores
		Apply Custom Image Processing Pipelines Using Combine and Transform
	Preprocess Volumes for Deep Learning
		Read Volumetric Data
		Associate Image and Label Data
		Preprocess Volumetric Data
	Preprocess Data for Domain-Specific Deep Learning Applications
		Image Processing Applications
		Object Detection
		Semantic Segmentation
		Signal Processing Applications
		Audio Processing Applications
		Text Analytics
	Develop Custom Mini-Batch Datastore
		Overview
		Implement MiniBatchable Datastore
		Add Support for Shuffling
		Validate Custom Mini-Batch Datastore
	Augment Images for Deep Learning Workflows Using Image Processing Toolbox
	Augment Pixel Labels for Semantic Segmentation
	Augment Bounding Boxes for Object Detection
	Prepare Datastore for Image-to-Image Regression
	Train Network Using Out-of-Memory Sequence Data
	Train Network Using Custom Mini-Batch Datastore for Sequence Data
	Classify Out-of-Memory Text Data Using Deep Learning
	Classify Out-of-Memory Text Data Using Custom Mini-Batch Datastore
	Data Sets for Deep Learning
		Image Data Sets
		Time Series and Signal Data Sets
		Video Data Sets
		Text Data Sets
		Audio Data Sets
Deep Learning Code Generation
	Code Generation for Deep Learning Networks
	Code Generation for Semantic Segmentation Network
	Lane Detection Optimized with GPU Coder
	Code Generation for a Sequence-to-Sequence LSTM Network
	Deep Learning Prediction on ARM Mali GPU
	Code Generation for Object Detection by Using YOLO v2
	Integrating Deep Learning with GPU Coder into Simulink
	Deep Learning Prediction by Using NVIDIA TensorRT
	Deep Learning Prediction by Using Different Batch Sizes
	Traffic Sign Detection and Recognition
	Logo Recognition Network
	Pedestrian Detection
	Code Generation for Denoising Deep Neural Network
	Train and Deploy Fully Convolutional Networks for Semantic Segmentation
	Code Generation for Semantic Segmentation Network by Using U-net
	Code Generation for Deep Learning on ARM Targets
	Code Generation for Deep Learning on Raspberry Pi
	Deep Learning Prediction with ARM Compute Using cnncodegen
	Deep Learning Prediction with Intel MKL-DNN
	Generate C++ Code for Object Detection Using YOLO v2 and Intel MKL-DNN
	Code Generation and Deployment of MobileNet-v2 Network to Raspberry Pi
Neural Network Design Book
Neural Network Objects, Data, and Training Styles
	Workflow for Neural Network Design
	Four Levels of Neural Network Design
	Neuron Model
		Simple Neuron
		Transfer Functions
		Neuron with Vector Input
	Neural Network Architectures
		One Layer of Neurons
		Multiple Layers of Neurons
		Input and Output Processing Functions
	Create Neural Network Object
	Configure Shallow Neural Network Inputs and Outputs
	Understanding Shallow Network Data Structures
		Simulation with Concurrent Inputs in a Static Network
		Simulation with Sequential Inputs in a Dynamic Network
		Simulation with Concurrent Inputs in a Dynamic Network
	Neural Network Training Concepts
		Incremental Training with adapt
		Batch Training
		Training Feedback
Multilayer Shallow Neural Networks and Backpropagation Training
	Multilayer Shallow Neural Networks and Backpropagation Training
	Multilayer Shallow Neural Network Architecture
		Neuron Model (logsig, tansig, purelin)
		Feedforward Neural Network
	Prepare Data for Multilayer Shallow Neural Networks
	Choose Neural Network Input-Output Processing Functions
		Representing Unknown or Don\'t-Care Targets
	Divide Data for Optimal Neural Network Training
	Create, Configure, and Initialize Multilayer Shallow Neural Networks
		Other Related Architectures
		Initializing Weights (init)
	Train and Apply Multilayer Shallow Neural Networks
		Training Algorithms
		Training Example
		Use the Network
	Analyze Shallow Neural Network Performance After Training
		Improving Results
	Limitations and Cautions
Dynamic Neural Networks
	Introduction to Dynamic Neural Networks
	How Dynamic Neural Networks Work
		Feedforward and Recurrent Neural Networks
		Applications of Dynamic Networks
		Dynamic Network Structures
		Dynamic Network Training
	Design Time Series Time-Delay Neural Networks
		Prepare Input and Layer Delay States
	Design Time Series Distributed Delay Neural Networks
	Design Time Series NARX Feedback Neural Networks
		Multiple External Variables
	Design Layer-Recurrent Neural Networks
	Create Reference Model Controller with MATLAB Script
	Multiple Sequences with Dynamic Neural Networks
	Neural Network Time-Series Utilities
	Train Neural Networks with Error Weights
	Normalize Errors of Multiple Outputs
	Multistep Neural Network Prediction
		Set Up in Open-Loop Mode
		Multistep Closed-Loop Prediction From Initial Conditions
		Multistep Closed-Loop Prediction Following Known Sequence
		Following Closed-Loop Simulation with Open-Loop Simulation
Control Systems
	Introduction to Neural Network Control Systems
	Design Neural Network Predictive Controller in Simulink
		System Identification
		Predictive Control
		Use the Neural Network Predictive Controller Block
	Design NARMA-L2 Neural Controller in Simulink
		Identification of the NARMA-L2 Model
		NARMA-L2 Controller
		Use the NARMA-L2 Controller Block
	Design Model-Reference Neural Controller in Simulink
		Use the Model Reference Controller Block
	Import-Export Neural Network Simulink Control Systems
		Import and Export Networks
		Import and Export Training Data
Radial Basis Neural Networks
	Introduction to Radial Basis Neural Networks
		Important Radial Basis Functions
	Radial Basis Neural Networks
		Neuron Model
		Network Architecture
		Exact Design (newrbe)
		More Efficient Design (newrb)
		Examples
	Probabilistic Neural Networks
		Network Architecture
		Design (newpnn)
	Generalized Regression Neural Networks
		Network Architecture
		Design (newgrnn)
Self-Organizing and Learning Vector Quantization Networks
	Introduction to Self-Organizing and LVQ
		Important Self-Organizing and LVQ Functions
	Cluster with a Competitive Neural Network
		Architecture
		Create a Competitive Neural Network
		Kohonen Learning Rule (learnk)
		Bias Learning Rule (learncon)
		Training
		Graphical Example
	Cluster with Self-Organizing Map Neural Network
		Topologies (gridtop, hextop, randtop)
		Distance Functions (dist, linkdist, mandist, boxdist)
		Architecture
		Create a Self-Organizing Map Neural Network (selforgmap)
		Training (learnsomb)
		Examples
	Learning Vector Quantization (LVQ) Neural Networks
		Architecture
		Creating an LVQ Network
		LVQ1 Learning Rule (learnlv1)
		Training
		Supplemental LVQ2.1 Learning Rule (learnlv2)
Adaptive Filters and Adaptive Training
	Adaptive Neural Network Filters
		Adaptive Functions
		Linear Neuron Model
		Adaptive Linear Network Architecture
		Least Mean Square Error
		LMS Algorithm (learnwh)
		Adaptive Filtering (adapt)
Advanced Topics
	Neural Networks with Parallel and GPU Computing
		Deep Learning
		Modes of Parallelism
		Distributed Computing
		Single GPU Computing
		Distributed GPU Computing
		Parallel Time Series
		Parallel Availability, Fallbacks, and Feedback
	Optimize Neural Network Training Speed and Memory
		Memory Reduction
		Fast Elliot Sigmoid
	Choose a Multilayer Neural Network Training Function
		SIN Data Set
		PARITY Data Set
		ENGINE Data Set
		CANCER Data Set
		CHOLESTEROL Data Set
		DIABETES Data Set
		Summary
	Improve Shallow Neural Network Generalization and Avoid Overfitting
		Retraining Neural Networks
		Multiple Neural Networks
		Early Stopping
		Index Data Division (divideind)
		Random Data Division (dividerand)
		Block Data Division (divideblock)
		Interleaved Data Division (divideint)
		Regularization
		Summary and Discussion of Early Stopping and Regularization
		Posttraining Analysis (regression)
	Edit Shallow Neural Network Properties
		Custom Network
		Network Definition
		Network Behavior
	Custom Neural Network Helper Functions
	Automatically Save Checkpoints During Neural Network Training
	Deploy Shallow Neural Network Functions
		Deployment Functions and Tools for Trained Networks
		Generate Neural Network Functions for Application Deployment
		Generate Simulink Diagrams
	Deploy Training of Shallow Neural Networks
Historical Neural Networks
	Historical Neural Networks Overview
	Perceptron Neural Networks
		Neuron Model
		Perceptron Architecture
		Create a Perceptron
		Perceptron Learning Rule (learnp)
		Training (train)
		Limitations and Cautions
	Linear Neural Networks
		Neuron Model
		Network Architecture
		Least Mean Square Error
		Linear System Design (newlind)
		Linear Networks with Delays
		LMS Algorithm (learnwh)
		Linear Classification (train)
		Limitations and Cautions
Neural Network Object Reference
	Neural Network Object Properties
		General
		Architecture
		Subobject Structures
		Functions
		Weight and Bias Values
	Neural Network Subobject Properties
		Inputs
		Layers
		Outputs
		Biases
		Input Weights
		Layer Weights
Function Approximation, Clustering, and Control Examples
	Body Fat Estimation
	Crab Classification
	Wine Classification
	Cancer Detection
	Character Recognition
	Train Stacked Autoencoders for Image Classification
	Iris Clustering
	Gene Expression Analysis
	Maglev Modeling
	Competitive Learning
	One-Dimensional Self-organizing Map
	Two-Dimensional Self-organizing Map
	Radial Basis Approximation
	Radial Basis Underlapping Neurons
	Radial Basis Overlapping Neurons
	GRNN Function Approximation
	PNN Classification
	Learning Vector Quantization
	Linear Prediction Design
	Adaptive Linear Prediction
	Classification with a 2-Input Perceptron
	Outlier Input Vectors
	Normalized Perceptron Rule
	Linearly Non-separable Vectors
	Pattern Association Showing Error Surface
	Training a Linear Neuron
	Linear Fit of Nonlinear Problem
	Underdetermined Problem
	Linearly Dependent Problem
	Too Large a Learning Rate
	Adaptive Noise Cancellation
Shallow Neural Networks Bibliography
	Shallow Neural Networks Bibliography
Mathematical Notation
	Mathematics and Code Equivalents
		Mathematics Notation to MATLAB Notation
		Figure Notation
Neural Network Blocks for the Simulink Environment
	Neural Network Simulink Block Library
		Transfer Function Blocks
		Net Input Blocks
		Weight Blocks
		Processing Blocks
	Deploy Shallow Neural Network Simulink Diagrams
		Example
		Suggested Exercises
		Generate Functions and Objects
Code Notes
	Deep Learning Toolbox Data Conventions
		Dimensions
		Variables




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